An improved optimal transport kernel embedding method with gating mechanism for singing voice separation and speaker identification

Singing voice separation (SVS) and speaker identification (SI) are two classic problems in speech signal processing. Deep neural networks (DNNs) solve these two problems by extracting effective representations of the target signal from the input mixture. Since essential features of a signal can be well reflected on its latent geometric structure of the feature distribution, a natural way to address SVS/SI is to extract the geometry-aware and distribution-related features of the target signal. To do this, this work introduces the concept of optimal transport (OT) to SVS/SI and proposes an improved optimal transport kernel embedding (iOTKE) to extract the target-distribution-related features. The iOTKE learns an OT from the input signal to the target signal on the basis of a reference set learned from all training data. Thus it can maintain the feature diversity and preserve the latent geometric structure of the distribution for the target signal. To further improve the feature selection ability, we extend the proposed iOTKE to a gated version, i.e., gated iOTKE (G-iOTKE), by incorporating a lightweight gating mechanism. The gating mechanism controls effective information flow and enables the proposed method to select important features for a specific input signal. We evaluated the proposed G-iOTKE on SVS/SI. Experimental results showed that the proposed method provided better results than other models.